Tabular Data Query Parameter Serialization
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Solution Overview
Problem
Conventional database management systems face inefficiencies when incorporating tabular data into query parameters, requiring disassembly into scalar values and resulting in longer queries and increased processing complexity.
Innovation Solution
The system serializes tabular data into volatile memory, copies it to a data server, and uses a hash join operation to efficiently determine associated rows in a database table, reducing memory usage and query complexity by employing metadata and value identifiers within the query syntax.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If tabular data is disassembled into scalar values and passed as query parameters, then the query can be executed by conventional database systems, but the query length increases and processing complexity increases
Solution Approach 1:
The tabular data is segmented into individual scalar values that are passed as separate query parameters to the database system. This segmentation allows conventional database systems to process the data while maintaining compatibility with existing SQL interfaces and execution engines.
Solution Approach 2:
The ABAP kernel acts as an intermediary that transforms tabular data into a format suitable for database processing. It disassembles the tabular structure into scalar parameters and reconstructs them in a way that reduces query complexity while maintaining database compatibility.
2Productivity
If tabular data is disassembled into scalar values, then the query can be processed by the database engine, but memory usage increases due to storing multiple scalar parameters
Solution Approach 1:
Instead of passing the entire tabular data structure to the database, the system creates a simplified copy in the form of scalar parameters. This copying approach enables database processing while reducing the memory footprint by eliminating the need to maintain the original tabular structure on both client and server sides.
3Reliability
If multiple query statements are generated to handle tabular data, then complete data retrieval is achieved, but the number of distinct operations increases and processing time increases
Solution Approach 1:
Multiple query operations are merged into a single optimized query statement. The system combines the logic of multiple distinct queries into one unified query that retrieves all required data in a single execution, thereby reducing processing time while maintaining complete data retrieval.
Data Source
AI summary
A system includes generation of a query to retrieve, from a first database table, a result set conforming to query parameters for all entries of a second table stored in a volatile memory of a query client, serialization of the second table into the volatile memory, copying of the serialized second table into a second volatile memory of a data server, de-serialization of the serialized second table into the second volatile memory, determination of a plurality of entries of the first database table which are associated with the second table, and determination of the result set from the plurality of entries based on the query parameters.